This is a guest blogpost by Vera Loftis, UK managing director, Bluewolf

The availability of real time customer data and the increasing capability of companies to analyse and create actionable insights is revolutionising the way companies interact with their customers. It is also transforming how and when colleagues with varied skill sets across different teams work together.

This e-guide collates a group of examples of big data technologies in use, such as how Mercedes-AMG Petronas Motorsport are looking to gain an edge on the competition in the Grand Prix season. Also see how big organisations are managing their big data operations and their data analytics programmes and teams through some high profile case studies.

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As our ability to make sense of customer data increases exponentially, marketing experts are collaborating far more closely with data analysts, scientists and IT teams than they have in the past.

In this area, augmented intelligence (AI) is set to assume ever greater importance. AI harnesses the power of information technology to enhance human intelligence. It helps companies to understand their customers’ habits, behaviours and actions to deliver a better overall customer experience. This in turn drives growth, efficiency and innovation.

Against this background, many of the world’s biggest brands need to examine multiple touchpoints across different parts of a customer’s journey. By looking at that holistically, and at what point customers engage or don’t engage in our communications, companies can make an impact and optimise on each unique ‘customer moment’. Moving forward, data scientists and IT professionals will perform a critical role in delivering high quality actionable customer insights.

The customer opportunity

There is a great opportunity for marketers to work closely with data analysts to understand their customers better. With such a raft of data available, the key is to make sense of it and learn more about what customers want. By understanding what products and services an individual wants, and when and through which channels, marketers can maximise engagement with customers and drive sales.

Customer journey mapping and its relevance

This is where customer journey mapping can play such a key role. Customer journey mapping is defined as the process of documenting a customer’s experiences with an organisation, across all major and minor touchpoints. Often done graphically, it can be a complex but very valuable activity that offers a holistic approach to understanding a customer’s experience. The more you use journey mapping, the better you can understand customers’ motivations and behaviours.

So how does AI fit into this picture?

AI can undoubtedly help organisations map the customer journey and, in doing so, understand and engage with them better. Augmented intelligence allows us to make sense of massive amounts of structured and unstructured data to get a clear picture of customer attitudes, motivations, and behaviours. If we can understand and then predict how data can benefit our customers, we can better serve them at a future moment of need.

For example, IBM and Salesforce’s recent partnership includes a way for IBM Watson and Salesforce Einstein to connect and enable deeper customer engagement. Watson can connect AI to weather through IBM Weather Insights. If say, an insurance company knew that hail was about to hit, through Salesforce connectivity, they can simply notify customers to get their cars into the garage. It’s that quick and actionable.

What’s next?

Through artificial intelligence and machine learning, marketers will soon be able to scale and automate campaigns at a level that we haven’t been able to before. There’s still a lot of things that need to be automated in creative and content marketing, but that’s the future. Data is the most important resource organisations have in delivering exceptional customer experiences. Working together across departments, companies need to focus on understanding the changing dynamics of their customers by mapping the customer journey and engaging them better with AI. By focusing on how they can use data and intelligent systems, they can understand customers better. This will help them deliver more relevant, timely and better products that increase customer satisfaction while upholding brand standards and maximising sales.

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